• Robert Laforce, Duygu Tosun, Pia Ghosh, Manja Lehmann, Cindee M Madison, Michael W Weiner, Bruce L Miller, William J Jagust, and Gil D Rabinovici.
• Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA ; Memory and Aging Center, Department of Neurology, University of California San Francisco, CA, USA.
• Neuroimage Clin. 2014 Jan 1; 4: 508-16.

AbstractThe relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ((11)C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA-AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology.

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